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Quantum embedding is indispensable for applying quantum machine learningtechniques to classical data, and has substantial impacts on performanceoutcomes. In this study, we present Neural Quantum Embedding (NQE), a methodthat efficiently optimizes quantum embedding by leveraging classical deeplearning techniques. NQE enhances the lower bound of the empirical risk,leading to substantial improvements in classification performance. Moreover,NQE improves robustness against noise. To validate the effectiveness of NQE, weconduct experiments on IBM quantum devices for image data classification,resulting in a remarkable accuracy enhancement from 0.52 to 0.96. Numericalanalysis of the local effective dimension highlights that NQE improves thetrainability and generalization performance of quantum neural networks.Furthermore, NQE achieves improved generalization in the quantum kernel method,as evidenced by a reduction in the upper bound of the expected risk.

Neural Quantum Embedding: Pushing the Limits of Quantum Supervised Learning